Propagation aspects of vehicle-to-vehicle communications - an overview

Molisch, Andreas F.; Tufvesson, Fredrik; Karedal, J.; Mecklenbräuker, Christoph F.

doi:10.1109/rws.2009.4957315

Cited by 39 publications

(25 citation statements)

References 36 publications

(58 reference statements)

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“…In the literature numerous models are available, mostly following the stochastic and the geometric approaches. Extensive overviews of these models can be found in [11] and [12]. We have picked the stochastic approach given its simplicity while maintaining good accuracy levels.…”

Section: Existing Channel Modelsmentioning

confidence: 99%

Node Density Estimation in VANETs Using Received Signal Power

Fontán¹,

Veeraraghava²,

Khomami³

2015

RADIOENGINEERING

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Abstract. Accurately estimating node density in Vehicular Ad hoc Networks, VANETs, is a challenging and crucial task. Various approaches exist, yet none takes advantage of physical layer parameters in a distributed fashion. This paper describes a framework that allows individual nodes to estimate the node density of their surrounding network independent of beacon messages and other infrastructurebased information. The proposal relies on three factors: 1) a discrete event simulator to estimate the average number of nodes transmitting simultaneously; 2) a realistic channel model for VANETs environment; and 3) a node density estimation technique. This work provides every vehicle on the road with two equations indicating the relation between 1) received signal strength versus simultaneously transmitting nodes, and 2) simultaneously transmitting nodes versus node density. Access to these equations enables individual nodes to estimate their real-time surrounding node density. The system is designed to work for the most complicated scenarios where nodes have no information about the topology of the network and, accordingly, the results indicate that the system is reasonably reliable and accurate. The outcome of this work has various applications and can be used for any protocol that is affected by node density.

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Section: Existing Channel Modelsmentioning

confidence: 99%

Node Density Estimation in VANETs Using Received Signal Power

Fontán¹,

Veeraraghava²,

Khomami³

2015

RADIOENGINEERING

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“…Channel Characterization of V2V propagation has been performed in various environments [13]. Different aspects of channels properties have been investigated in measurementbased studies, e.g.…”

Section: Related Workmentioning

confidence: 99%

Improving the accuracy of environment-specific vehicular channel modeling

Wang

Anderson

Steenkiste

et al. 2012

Proceedings of the Seventh ACM International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterizatio

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Networking research benefits from the controlled, repeatable experimentation provided by simulation and emulation systems. Making such simulations realistic is a challenge for wireless systems and is especially difficult for vehicular networks. This paper introduces a realistic vehicular channel simulation model that includes a Line-Of-Sight (LOS ) module and Vehicle-to-Vehicle (V2V) fading module specially designed for vehicular channels.Specialized models can provide a close approximation of real channel conditions, but their accuracy is limited by the quality of input information about the environment being modeled. In this paper, we present a systematic approach to estimate location-specific scattering properties using aerial photography. We show that this approach significantly improves the accuracy of simulated channel fading.

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“…The signal is then transmitted through a multi-path time-varying channel which has independent taps with average power of the ℎ tap denoted by 2 . In the case both transmitter and receiver are moving, accurate mathematical model to describe this non-stationary channel is not available yet [14], [15]. The scenario with moving scattering is believed to be more complicated.…”

Section: System Modelmentioning

confidence: 99%

Semiblind Iterative Data Detection for OFDM Systems with CFO and Doubly Selective Channels

He¹,

Ma²,

Wu³

et al. 2010

IEEE Trans. Commun.

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Abstract-Data detection for OFDM systems over unknown doubly selective channels (DSCs) and carrier frequency offset (CFO) is investigated. A semiblind iterative detection algorithm is developed based on the expectation-maximization (EM) algorithm. It iteratively estimates the CFO, channel and recovers the unknown data using only limited number of pilot subcarriers in one OFDM symbol. In addition, efficient initial CFO and channel estimates are also derived based on approximated maximum likelihood (ML) and minimum mean square error (MMSE) criteria respectively. Simulation results show that the proposed data detection algorithm converges in a few iterations and moreover, its performance is close to the ideal case with perfect CFO and channel state information.Index Terms-Carrier frequency offset (CFO), orthogonal frequency division multiplexing (OFDM), doubly selective channel (DSC), data detection, expectation-maximization (EM).

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Propagation aspects of vehicle-to-vehicle communications - an overview

Cited by 39 publications

References 36 publications

Node Density Estimation in VANETs Using Received Signal Power

Node Density Estimation in VANETs Using Received Signal Power

Improving the accuracy of environment-specific vehicular channel modeling

Semiblind Iterative Data Detection for OFDM Systems with CFO and Doubly Selective Channels

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